Microarrays doi: 10.3390/microarrays6020009

Authors: Ahsan Munir Hassan Waseem Maggie Williams Robert Stedtfeld Erdogan Gulari James Tiedje Syed Hashsham

Microfluidic DNA biochips capable of detecting specific DNA sequences are useful in medical diagnostics, drug discovery, food safety monitoring and agriculture. They are used as miniaturized platforms for analysis of nucleic acids-based biomarkers. Binding kinetics between immobilized single stranded DNA on the surface and its complementary strand present in the sample are of interest. To achieve optimal sensitivity with minimum sample size and rapid hybridization, ability to predict the kinetics of hybridization based on the thermodynamic characteristics of the probe is crucial. In this study, a computer aided numerical model for the design and optimization of a flow-through biochip was developed using a finite element technique packaged software tool (FEMLAB; package included in COMSOL Multiphysics) to simulate the transport of DNA through a microfluidic chamber to the reaction surface. The model accounts for fluid flow, convection and diffusion in the channel and on the reaction surface. Concentration, association rate constant, dissociation rate constant, recirculation flow rate, and temperature were key parameters affecting the rate of hybridization. The model predicted the kinetic profile and signal intensities of eighteen 20-mer probes targeting vancomycin resistance genes (VRGs). Predicted signal intensities and hybridization kinetics strongly correlated with experimental data in the biochip (R2 = 0.8131).

Microarrays doi: 10.3390/microarrays6020008

Authors: Jinglin Fu

Recently, peptide microarrays have been used to distinguish proteins, antibodies, viruses, and bacteria based on their binding to random sequence peptides. We reported on the use of peptide arrays to identify enzyme modulators that involve screening an array of 10,000 defined and addressable peptides on a microarray. Primary peptides were first selected to inhibit the enzyme at low μM concentrations. Then, new peptides were found to only bind strongly with the enzyme–inhibitor complex, but not the native enzyme. These new peptides served as secondary inhibitors that enhanced the inhibition of the enzyme together with the primary peptides. Without the primary peptides, the secondary effect peptides had little effect on the enzyme activity. Conversely, we also selected peptides that recovered the activities of inhibited enzyme–peptide complex. The selection of cooperative peptide pairs will provide a versatile toolkit for modulating enzyme functions, which may potentially be applied to drug discovery and biocatalysis.

Microarrays doi: 10.3390/microarrays6020007

Authors: Mary Shannon Byers Christianna Howard Xiaofei Wang

The GLUT members belong to a family of glucose transporter proteins that facilitate glucose transport across the cell membrane. The mammalian GLUT family consists of thirteen members (GLUTs 1–12 and H+-myo-inositol transporter (HMIT)). Humans have a recently duplicated GLUT member, GLUT14. Avians express the majority of GLUT members. The arrangement of multiple GLUTs across all somatic tissues signifies the important role of glucose across all organisms. Defects in glucose transport have been linked to metabolic disorders, insulin resistance and diabetes. Despite the essential importance of these transporters, our knowledge regarding GLUT members in avians is fragmented. It is clear that there are no chicken orthologs of mammalian GLUT4 and GLUT7. Our examination of GLUT members in the chicken revealed that some chicken GLUT members do not have corresponding orthologs in mammals. We review the information regarding GLUT orthologs and their function and expression in mammals and birds, with emphasis on chickens and humans.

Microarrays doi: 10.3390/microarrays6010006

Authors: Ben Tall Jayanthi Gangiredla Christopher Grim Isha Patel Scott Jackson Mark Mammel Mahendra Kothary Venugopal Sathyamoorthy Laurenda Carter Séamus Fanning Carol Iversen Franco Pagotto Roger Stephan Angelika Lehner Jeffery Farber Qiong Yan Gopal Gopinath

Cronobacter (previously known as Enterobacter sakazakii) is a genus of Gram-negative, facultatively anaerobic, oxidase-negative, catalase-positive, rod-shaped bacteria of the family Enterobacteriaceae. These organisms cause a variety of illnesses such as meningitis, necrotizing enterocolitis, and septicemia in neonates and infants, and urinary tract, wound, abscesses or surgical site infections, septicemia, and pneumonia in adults. The total gene content of 379 strains of Cronobacter spp. and taxonomically-related isolates was determined using a recently reported DNA microarray. The Cronobacter microarray as a genotyping tool gives the global food safety community a rapid method to identify and capture the total genomic content of outbreak isolates for food safety, environmental, and clinical surveillance purposes. It was able to differentiate the seven Cronobacter species from one another and from non-Cronobacter species. The microarray was also able to cluster strains within each species into well-defined subgroups. These results also support previous studies on the phylogenic separation of species members of the genus and clearly highlight the evolutionary sequence divergence among each species of the genus compared to phylogenetically-related species. This review extends these studies and illustrates how the microarray can also be used as an investigational tool to mine genomic data sets from strains. Three case studies describing the use of the microarray are shown and include: (1) the determination of allelic differences among Cronobacter sakazakii strains possessing the virulence plasmid pESA3; (2) mining of malonate and myo-inositol alleles among subspecies of Cronobacter dublinensis strains to determine subspecies identity; and (3) lastly using the microarray to demonstrate sequence divergence and phylogenetic relatedness trends for 13 outer-membrane protein alleles among 240 Cronobacter and phylogenetically-related strains. The goal of this review is to describe microarrays as a robust tool for genomics research of this assorted and important genus, a criterion toward the development of future preventative measures to eliminate this foodborne pathogen from the global food supply.

Microarrays doi: 10.3390/microarrays6010005

Authors: Diana Kelmansky Lila Ricci

The traditional approach with microarray data has been to apply transformations that approximately normalize them, with the drawback of losing the original scale. The alternative stand point taken here is to search for models that fit the data, characterized by the presence of negative values, preserving their scale; one advantage of this strategy is that it facilitates a direct interpretation of the results. A new family of distributions named gpower-normal indexed by p∈R is introduced and it is proven that these variables become normal or truncated normal when a suitable gpower transformation is applied. Expressions are given for moments and quantiles, in terms of the truncated normal density. This new family can be used to model asymmetric data that include non-positive values, as required for microarray analysis. Moreover, it has been proven that the gpower-normal family is a special case of pseudo-dispersion models, inheriting all the good properties of these models, such as asymptotic normality for small variances. A combined maximum likelihood method is proposed to estimate the model parameters, and it is applied to microarray and contamination data. Rcodes are available from the authors upon request.

Microarrays doi: 10.3390/microarrays6010004

Authors: Ryoiti Kiyama

The application of DNA microarray assay (DMA) has entered a new era owing to recent innovations in omics technologies. This review summarizes recent applications of DMA‐based gene expression profiling by focusing on the screening and characterizationof traditional Chinese medicine. First, herbs, mushrooms, and dietary plants analyzed by DMA along with their effective components and their biological/physiological effects are summarized and discussed by examining their comprehensive list and a list of representative effective chemicals. Second, the mechanisms of action of traditional Chinese medicine are summarized by examining the genes and pathways responsible for the action, the cell functions involved in the action, and the activities found by DMA (silent estrogens). Third, applications of DMA for traditional Chinese medicine are discussed by examining reported examples and new protocols for its use in quality control. Further innovations in the signaling pathway based evaluation of beneficial effects and the assessment of potential risks of traditional Chinese medicine are expected, just as are observed in other closely related fields, such as the therapeutic, environmental, nutritional, and pharmacological fields.

Microarrays doi: 10.3390/microarrays6010003

Authors: Robert Hamilton

IgE antibodies serve as the gatekeeper for the release of mediators from sensitized (IgE positive) mast cells and basophils following a relevant allergen exposure which can lead to an immediate-type hypersensitivity (allergic) reaction. Purified recombinant and native allergens were combined in the 1990s with state of the art chip technology to establish the first microarray-based IgE antibody assay. Triplicate spots to over 100 allergenic molecules are immobilized on an amine-activated glass slide to form a single panel multi-allergosorbent assay. Human antibodies, typically of the IgE and IgG isotypes, specific for one or many allergens bind to their respective allergen(s) on the chip. Following removal of unbound serum proteins, bound IgE antibody is detected with a fluorophore-labeled anti-human IgE reagent. The fluorescent profile from the completed slide provides a sensitization profile of an allergic patient which can identify IgE antibodies that bind to structurally similar (cross-reactive) allergen families versus molecules that are unique to a single allergen specificity. Despite its ability to rapidly analyze many IgE antibody specificities in a single simple assay format, the chip-based microarray remains less analytically sensitive and quantitative than its singleplex assay counterpart (ImmunoCAP, Immulite). Microgram per mL quantities of allergen-specific IgG antibody can also complete with nanogram per mL quantities of specific IgE for limited allergen binding sites on the chip. Microarray assays, while not used in clinical immunology laboratories for routine patient IgE antibody testing, will remain an excellent research tool for defining sensitization profiles of populations in epidemiological studies.

Microarrays doi: 10.3390/microarrays6010002

Authors: Microarrays Editorial Office

The editors of Microarrays would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...]

Microarrays doi: 10.3390/microarrays6010001

Authors: Markus Roucka Klaus Zimmermann Markus Fido Andreas Nechansky

Lectin microarray technology was applied to compare the glycosylation pattern of the monoclonal antibody MB311 expressed in SP2.0 cells to an antibody-dependent cellular cytotoxic effector function (ADCC)-optimized variant (MB314). MB314 was generated by a plant expression system that uses genetically modified moss protoplasts (Physcomitrella patens) to generate a de-fucosylated version of MB311. In contrast to MB311, no or very low interactions of MB314 with lectins Aspergillus oryzae l-fucose (AOL), Pisum sativum agglutinin (PSA), Lens culinaris agglutinin (LCA), and Aleuria aurantia lectin (AAL) were observed. These lectins are specific for mono-/biantennary N-glycans containing a core fucose residue. Importantly, this fucose indicative lectin-binding pattern correlated with increased MB314 binding to CD16 (FcγRIII; receptor for the constant region of an antibody)—whose affinity is mediated through core fucosylation—and stronger ADCC. In summary, these results demonstrate that lectin microarrays are useful orthogonal methods during antibody development and for characterization.

Microarrays doi: 10.3390/microarrays5040029

Authors: Pietro Guzzi Giuseppe Tradigo Pierangelo Veltri

MicroRNAs (miRNAs) are small biological molecules that play an important role during the mechanisms of protein formation. Recent findings have demonstrated that they act as both positive and negative regulators of protein formation. Thus, the investigation of miRNAs, i.e., the determination of their level of expression, has developed a huge interest in the scientific community. One of the leading technologies for extracting miRNA data from biological samples is the miRNA Affymetrix platform. It provides the quantification of the level of expression of the miRNA in a sample, thus enabling the accumulation of data and allowing the determination of relationships among miRNA, genes, and diseases. Unfortunately, there is a lack of a comprehensive platform able to provide all the functions needed for the extraction of information from miRNA data. We here present miRNA-Analyzer, a complete software tool providing primary functionalities for miRNA data analysis. The current version of miRNA-Analyzer wraps the Affymetrix QCTool for the preprocessing of binary data files, and then provides feature selection (the filtering by species and by the associated p-value of preprocessed files). Finally, preprocessed and filtered data are analyzed by the Multiple Experiment Viewer (T-MEV) and Short Time Series Expression Miner (STEM) tools, which are also wrapped into miRNA-Analyzer, thus providing a unique environment for miRNA data analysis. The tool offers a plug-in interface so it is easily extensible by adding other algorithms as plug-ins. Users may download the tool freely for academic use at https://sites.google.com/site/mirnaanalyserproject/d.

Microarrays doi: 10.3390/microarrays5040028

Authors: Gabriella Jogia Tina Tronser Anna Popova Pavel Levkin

Single-cell analysis provides fundamental information on individual cell response to different environmental cues and is a growing interest in cancer and stem cell research. However, current existing methods are still facing challenges in performing such analysis in a high-throughput manner whilst being cost-effective. Here we established the Droplet Microarray (DMA) as a miniaturized screening platform for high-throughput single-cell analysis. Using the method of limited dilution and varying cell density and seeding time, we optimized the distribution of single cells on the DMA. We established culturing conditions for single cells in individual droplets on DMA obtaining the survival of nearly 100% of single cells and doubling time of single cells comparable with that of cells cultured in bulk cell population using conventional methods. Our results demonstrate that the DMA is a suitable platform for single-cell analysis, which carries a number of advantages compared with existing technologies allowing for treatment, staining and spot-to-spot analysis of single cells over time using conventional analysis methods such as microscopy.

Microarrays doi: 10.3390/microarrays5040027

Authors: Jari Louhelainen

The papers published in this Special Issue “SNP arrays” (Single Nucleotide Polymorphism Arrays) focus on several perspectives associated with arrays of this type. The range of papers vary from a case report to reviews, thereby targeting wider audiences working in this field. The research focus of SNP arrays is often human cancers but this Issue expands that focus to include areas such as rare conditions, animal breeding and bioinformatics tools. Given the limited scope, the spectrum of papers is nothing short of remarkable and even from a technical point of view these papers will contribute to the field at a general level. Three of the papers published in this Special Issue focus on the use of various SNP array approaches in the analysis of three different cancer types. Two of the papers concentrate on two very different rare conditions, applying the SNP arrays slightly differently. Finally, two other papers evaluate the use of the SNP arrays in the context of genetic analysis of livestock. The findings reported in these papers help to close gaps in the current literature and also to give guidelines for future applications of SNP arrays.

Microarrays doi: 10.3390/microarrays5040026

Authors: Heather Ruskin

High-throughput microarray technologies have long been a source of data for a wide range of biomedical investigations. Over the decades, variants have been developed and sophistication of measurements has improved, with generated data providing both valuable insight and considerable analytical challenge. The cost-effectiveness of microarrays, as well as their fundamental applicability, made them a first choice for much early genomic research and efforts to improve accessibility, quality and interpretation have continued unabated. In recent years, however, the emergence of new generations of sequencing methods and, importantly, reduction of costs, has seen a preferred shift in much genomic research to the use of sequence data, both less ‘noisy’ and, arguably, with species information more directly targeted and easily interpreted. Nevertheless, new microarray data are still being generated and, together with their considerable legacy, can offer a complementary perspective on biological systems and disease pathogenesis. The challenge now is to exploit novel methods for enhancing and combining these data with those generated by alternative high-throughput techniques, such as sequencing, to provide added value. Augmentation and integration of microarray data and the new horizons this opens up, provide the theme for the papers in this Special Issue.

Microarrays doi: 10.3390/microarrays5040025

Authors: Julie Fredonnet Julie Foncy Jean-Christophe Cau Childérick Séverac Jean François Emmanuelle Trévisiol

Microarrays are established research tools for genotyping, expression profiling, or molecular diagnostics in which DNA molecules are precisely addressed to the surface of a solid support. This study assesses the fabrication of low-density oligonucleotide arrays using an automated microcontact printing device, the InnoStamp 40®. This automate allows a multiplexed deposition of oligoprobes on a functionalized surface by the use of a MacroStampTM bearing 64 individual pillars each mounted with 50 circular micropatterns (spots) of 160 µm diameter at 320 µm pitch. Reliability and reuse of the MacroStampTM were shown to be fast and robust by a simple washing step in 96% ethanol. The low-density microarrays printed on either epoxysilane or dendrimer-functionalized slides (DendriSlides) showed excellent hybridization response with complementary sequences at unusual low probe and target concentrations, since the actual probe density immobilized by this technology was at least 10-fold lower than with the conventional mechanical spotting. In addition, we found a comparable hybridization response in terms of fluorescence intensity between spotted and printed oligoarrays with a 1 nM complementary target by using a 50-fold lower probe concentration to produce the oligoarrays by the microcontact printing method. Taken together, our results lend support to the potential development of this multiplexed microcontact printing technology employing soft lithography as an alternative, cost-competitive tool for fabrication of low-density DNA microarrays.

Microarrays doi: 10.3390/microarrays5040024

Authors: Giuseppe Agapito Cirino Botta Pietro Guzzi Mariamena Arbitrio Maria Di Martino Pierfrancesco Tassone Pierosandro Tagliaferri Mario Cannataro

Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients.

Microarrays doi: 10.3390/microarrays5030023

Authors: Sriram Chockalingam Maneesha Aluru Srinivas Aluru

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

Microarrays doi: 10.3390/microarrays5030021

Authors: Bahman Delalat Darling Rojas-Canales Soraya Rasi Ghaemi Michaela Waibel Frances Harding Daniella Penko Christopher Drogemuller Thomas Loudovaris Patrick Coates Nicolas Voelcker

Pancreatic islet transplantation has become a recognized therapy for insulin-dependent diabetes mellitus. During isolation from pancreatic tissue, the islet microenvironment is disrupted. The extracellular matrix (ECM) within this space not only provides structural support, but also actively signals to regulate islet survival and function. In addition, the ECM is responsible for growth factor presentation and sequestration. By designing biomaterials that recapture elements of the native islet environment, losses in islet function and number can potentially be reduced. Cell microarrays are a high throughput screening tool able to recreate a multitude of cellular niches on a single chip. Here, we present a screening methodology for identifying components that might promote islet survival. Automated fluorescence microscopy is used to rapidly identify islet derived cell interaction with ECM proteins and immobilized growth factors printed on arrays. MIN6 mouse insulinoma cells, mouse islets and, finally, human islets are progressively screened. We demonstrate the capability of the platform to identify ECM and growth factor protein candidates that support islet viability and function and reveal synergies in cell response.

Microarrays doi: 10.3390/microarrays5030022

Authors: Muhammad Rizwan Bengt Rönnberg Maksims Cistjakovs Åke Lundkvist Rudiger Pipkorn Jonas Blomberg

Background: Antibodies to microbes, or to autoantigens, are important markers of disease. Antibody detection (serology) can reveal both past and recent infections. There is a great need for development of rational ways of detecting and quantifying antibodies, both for humans and animals. Traditionally, serology using synthetic antigens covers linear epitopes using up to 30 amino acid peptides. Methods: We here report that peptides of 100 amino acids or longer (“megapeptides”), designed and synthesized for optimal serological performance, can successfully be used as detection antigens in a suspension multiplex immunoassay (SMIA). Megapeptides can quickly be created just from pathogen sequences. A combination of rational sequencing and bioinformatic routines for definition of diagnostically-relevant antigens can, thus, rapidly yield efficient serological diagnostic tools for an emerging infectious pathogen. Results: We designed megapeptides using bioinformatics and viral genome sequences. These long peptides were tested as antigens for the presence of antibodies in human serum to the filo-, herpes-, and polyoma virus families in a multiplex microarray system. All of these virus families contain recently discovered or emerging infectious viruses. Conclusion: Long synthetic peptides can be useful as serological diagnostic antigens, serving as biomarkers, in suspension microarrays.

Microarrays doi: 10.3390/microarrays5030020

Authors: Alexander Kwarteng Samuel Ahuno

Data obtained from expression microarrays enables deeper understanding of the molecular signatures of infectious diseases. It provides rapid and accurate information on how infections affect the clustering of gene expression profiles, pathways and networks that are transcriptionally active during various infection states compared to conventional diagnostic methods, which primarily focus on single genes or proteins. Thus, microarray technologies offer advantages in understanding host-parasite interactions associated with filarial infections. More importantly, the use of these technologies can aid diagnostics and helps translate current genomic research into effective treatment and interventions for filarial infections. Studying immune responses via microarray following infection can yield insight into genetic pathways and networks that can have a profound influence on the development of anti-parasitic vaccines.

Microarrays doi: 10.3390/microarrays5030019

Authors: Martin Sill Christoph Schröder Ying Shen Aseel Marzoq Radovan Komel Jörg Hoheisel Henrik Nienhüser Thomas Schmidt Damjana Kastelic

In this study, protein profiling was performed on gastric cancer tissue samples in order to identify proteins that could be utilized for an effective diagnosis of this highly heterogeneous disease and as targets for therapeutic approaches. To this end, 16 pairs of postoperative gastric adenocarcinomas and adjacent non-cancerous control tissues were analyzed on microarrays that contain 813 antibodies targeting 724 proteins. Only 17 proteins were found to be differentially regulated, with much fewer molecules than the numbers usually identified in studies comparing tumor to healthy control tissues. Insulin-like growth factor-binding protein 7 (IGFBP7), S100 calcium binding protein A9 (S100A9), interleukin-10 (IL‐10) and mucin 6 (MUC6) exhibited the most profound variations. For an evaluation of the proteins’ capacity for discriminating gastric cancer, a Receiver Operating Characteristic curve analysis was performed, yielding an accuracy (area under the curve) value of 89.2% for distinguishing tumor from non-tumorous tissue. For confirmation, immunohistological analyses were done on tissue slices prepared from another cohort of patients with gastric cancer. The utility of the 17 marker proteins, and particularly the four molecules with the highest specificity for gastric adenocarcinoma, is discussed for them to act as candidates for diagnosis, even in serum, and targets for therapeutic approaches.

Microarrays doi: 10.3390/microarrays5030018

Authors: Orazio Fortunato Carla Verri Ugo Pastorino Gabriella Sozzi Mattia Boeri

Lung cancer is the most common cause of cancer deaths worldwide. MicroRNAs (miRNAs) are short, non-coding RNAs that regulate gene expression. Many studies have reported that alterations in miRNA expression are involved in several human tumors. We have previously identified a circulating miRNA signature classifier (MSC) able to discriminate lung cancer with more aggressive features. In the present work, microarray miRNA profiling of tumor tissues collected from 19 lung cancer patients with an available MSC result were perform in order to find a possible association between miRNA expression and the MSC risk level. Eleven tissue mature miRNAs and six miRNA precursors were observed to be associated with the plasma MSC risk level of patients. Not one of these miRNAs was included in the MSC algorithm. A pathway enrichment analysis revealed a role of these miRNA in the main pathways determining lung cancer aggressiveness. Overall, these findings add to the knowledge that tissue and plasma miRNAs behave as excellent diagnostic and prognostic biomarkers, which may find rapid application in clinical settings.

Microarrays doi: 10.3390/microarrays5020017

Authors: Ezequiel Nicolazzi Gabriele Marras Alessandra Stella

One of the main advantages of single nucleotide polymorphism (SNP) array technology is providing genotype calls for a specific number of SNP markers at a relatively low cost. Since its first application in animal genetics, the number of available SNP arrays for each species has been constantly increasing. However, conversely to that observed in whole genome sequence data analysis, SNP array data does not have a common set of file formats or coding conventions for allele calling. Therefore, the standardization and integration of SNP array data from multiple sources have become an obstacle, especially for users with basic or no programming skills. Here, we describe the difficulties related to handling SNP array data, focusing on file formats, SNP allele coding, and mapping. We also present SNPConvert suite, a multi-platform, open-source, and user-friendly set of tools to overcome these issues. This tool, which can be integrated with open-source and open-access tools already available, is a first step towards an integrated system to standardize and integrate any type of raw SNP array data. The tool is available at: https://github. com/nicolazzie/SNPConvert.git.

Microarrays doi: 10.3390/microarrays5020016

Authors: Anna Gerdtsson Linda Dexlin-Mellby Payam Delfani Erica Berglund Carl Borrebaeck Christer Wingren

Antibody microarrays have emerged as an important tool within proteomics, enabling multiplexed protein expression profiling in both health and disease. The design and performance of antibody microarrays and how they are processed are dependent on several factors, of which the interplay between the antibodies and the solid surfaces plays a central role. In this study, we have taken on the first comprehensive view and evaluated the overall impact of solid surfaces on the recombinant antibody microarray design. The results clearly demonstrated the importance of the surface-antibody interaction and showed the effect of the solid supports on the printing process, the array format of planar arrays (slide- and well-based), the assay performance (spot features, reproducibility, specificity and sensitivity) and assay processing (degree of automation). In the end, two high-end recombinant antibody microarray technology platforms were designed, based on slide-based (black polymer) and well-based (clear polymer) arrays, paving the way for future large-scale protein expression profiling efforts.

Microarrays doi: 10.3390/microarrays5020015

Authors: Margherita Squillario Matteo Barbieri Alessandro Verri Annalisa Barla

Biological interpretability is a key requirement for the output of microarray data analysis pipelines. The most used pipeline first identifies a gene signature from the acquired measurements and then uses gene enrichment analysis as a tool for functionally characterizing the obtained results. Recently Knowledge Driven Variable Selection (KDVS), an alternative approach which performs both steps at the same time, has been proposed. In this paper, we assess the effectiveness of KDVS against standard approaches on a Parkinson’s Disease (PD) dataset. The presented quantitative analysis is made possible by the construction of a reference list of genes and gene groups associated to PD. Our work shows that KDVS is much more effective than the standard approach in enhancing the interpretability of the obtained results.

Microarrays doi: 10.3390/microarrays5020014

Authors: Suneth Perera Bin Wang Arturo Damian Wayne Dyer Li Zhou Viviane Conceicao Nitin Saksena

Background: HIV p24 is an extracellular HIV antigen involved in viral replication. Falling p24 antibody responses are associated with clinical disease progression and their preservation with non-progressive disease. Stimulation of p24 antibody production by immunization to delay progression was the basis of discontinued p24 vaccine. We studied a therapy-naive HIV+ man from Sydney, Australia, infected in 1988. He received the HIV-p24-virus like particle (VLP) vaccine in 1993, and continues to show vigorous p24 antigen responses (>4% p24-specific CD4+ T cells), coupled with undetectable plasma viremia. We defined immune-protective correlates of p24 vaccination at the proteomic level through parallel retrospective analysis of cellular immune responses to p24 antigen in CD4+ and CD8+ T cells and CD14+ monocytes at viremic and aviremic phases using antibody-array. We found statistically significant coordinated up-regulation by all three cell-types with high fold-changes in fractalkine, ITAC, IGFBP-2, and MIP-1α in the aviremic phase. TECK and TRAIL-R4 were down-regulated in the viremic phase and up-regulated in the aviremic phase. The up-regulation of fractalkine in all three cell-types coincided with protective effect, whereas the dysfunction in anti-apoptotic chemokines with the loss of immune function. This study highlights the fact that induction of HIV-1-specific helper cells together with coordinated cellular immune response (p < 0.001) might be important in immunotherapeutic interventions and HIV vaccine development.

Microarrays doi: 10.3390/microarrays5020012

Authors: Al Shahandeh Daniel Johnstone Joshua Atkins Jean-Marie Sontag Moones Heidari Nilofar Daneshi Elvis Freeman-Acquah Elizabeth Milward

As recognised by the National Institutes of Health (NIH) Precision Medicine Initiative (PMI), microarray technology currently provides a rapid, inexpensive means of identifying large numbers of known genomic variants or gene transcripts in experimental and clinical settings. However new generation sequencing techniques are now being introduced in many clinical genetic contexts, particularly where novel mutations are involved. While these methods can be valuable for screening a restricted set of genes for known or novel mutations, implementation of whole genome sequencing in clinical practice continues to present challenges. Even very accurate high-throughput methods with small error rates can generate large numbers of false negative or false positive errors due to the high numbers of simultaneous readings. Additional validation is likely to be required for safe use of any such methods in clinical settings. Custom-designed arrays can offer advantages for screening for common, known mutations and, in this context, may currently be better suited for accredited, quality-controlled clinical genetic screening services, as illustrated by their successful application in several large-scale pre-emptive pharmacogenomics programs now underway. Excessive, inappropriate use of next-generation sequencing may waste scarce research funds and other resources. Microarrays presently remain the technology of choice in applications that require fast, cost-effective genome-wide screening of variants of known importance, particularly for large sample sizes. This commentary considers some of the applications where microarrays continue to offer advantages over next-generation sequencing technologies.

Microarrays doi: 10.3390/microarrays5020013

Authors: Chris Evelyn Erika Lisabeth Susan Wade Andrew Haak Craig Johnson Elizabeth Lawlor Richard Neubig

Metastasis is the major cause of cancer deaths and control of gene transcription has emerged as a critical contributing factor. RhoA- and RhoC-induced gene transcription via the actin-regulated transcriptional co-activator megakaryocytic leukemia (MKL) and serum response factor (SRF) drive metastasis in breast cancer and melanoma. We recently identified a compound, CCG-1423, which blocks Rho/MKL/SRF-mediated transcription and inhibits PC-3 prostate cancer cell invasion. Here, we undertook a genome-wide expression study in PC-3 cells to explore the mechanism and function of this compound. There was significant overlap in the genes modulated by CCG-1423 and Latrunculin B (Lat B), which blocks the Rho/MKL/SRF pathway by preventing actin polymerization. In contrast, the general transcription inhibitor 5,6-dichloro-1-β-d-ribofuranosyl-1H-benzimidazole (DRB) showed a markedly different pattern. Effects of CCG-1423 and Lat B on gene expression correlated with literature studies of MKL knock-down. Gene sets involved in DNA synthesis and repair, G1/S transition, and apoptosis were modulated by CCG-1423. It also upregulated genes involved in endoplasmic reticulum stress. Targets of the known Rho target transcription factor family E2F and genes related to melanoma progression and metastasis were strongly suppressed by CCG-1423. These results confirm the ability of our compound to inhibit expression of numerous Rho/MKL-dependent genes and show effects on stress pathways as well. This suggests a novel approach to targeting aggressive cancers and metastasis.

Microarrays doi: 10.3390/microarrays5020011

Authors: Rebecca Jonczyk Tracy Kurth Antonina Lavrentieva Johanna-Gabriela Walter Thomas Scheper Frank Stahl

Living cell microarrays are a highly efficient cellular screening system. Due to the low number of cells required per spot, cell microarrays enable the use of primary and stem cells and provide resolution close to the single-cell level. Apart from a variety of conventional static designs, microfluidic microarray systems have also been established. An alternative format is a microarray consisting of three-dimensional cell constructs ranging from cell spheroids to cells encapsulated in hydrogel. These systems provide an in vivo-like microenvironment and are preferably used for the investigation of cellular physiology, cytotoxicity, and drug screening. Thus, many different high-tech microarray platforms are currently available. Disadvantages of many systems include their high cost, the requirement of specialized equipment for their manufacture, and the poor comparability of results between different platforms. In this article, we provide an overview of static, microfluidic, and 3D cell microarrays. In addition, we describe a simple method for the printing of living cell microarrays on modified microscope glass slides using standard DNA microarray equipment available in most laboratories. Applications in research and diagnostics are discussed, e.g., the selective and sensitive detection of biomarkers. Finally, we highlight current limitations and the future prospects of living cell microarrays.

Microarrays doi: 10.3390/microarrays5020010

Authors: Giuseppe Merlino Patrizia Miodini Biagio Paolini Maria Carcangiu Massimiliano Gennaro Matteo Dugo Maria Daidone Vera Cappelletti

Background: The tumor microenvironment participates in the regulation of tumor progression and influences treatment sensitivity. In breast cancer, it also may play a role in determining the fate of non-invasive lesions such as ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive diseases, which is aggressively treated despite its indolent nature in many patients since no biomarkers are available to predict the progression of DCIS to invasive disease. In vitro models of stromal activation by breast tumor cells might provide clues as to specific stromal genes crucial for the transition from DCIS to invasive disease. Methods: normal human dermal fibroblasts (NHDF) were treated under serum-free conditions with cell culture media conditioned by breast cancer cell lines (SkBr3, MDA-MB-468, T47D) for 72 h and subjected to gene expression profiling with Illumina platform. Results: TGM2, coding for a tissue transglutaminase, was identified as candidate gene for stromal activation. In public transcriptomic datasets of invasive breast tumors TGM2 expression proved to provide prognostic information. Conversely, its role as an early biosensor of tumor invasiveness needs to be further investigated by in situ analyses. Conclusion: Stromal TGM2 might probably be associated with precancerous evolution at earlier stages compared to DCIS.

Microarrays doi: 10.3390/microarrays5020009

Authors: Radha Iyer Ira Schwartz

Borrelia burgdorferi, the spirochetal agent of Lyme disease, is maintained in nature in a cycle involving a tick vector and a mammalian host. Adaptation to the diverse conditions of temperature, pH, oxygen tension and nutrient availability in these two environments requires the precise orchestration of gene expression. Over 25 microarray analyses relating to B. burgdorferi genomics and transcriptomics have been published. The majority of these studies has explored the global transcriptome under a variety of conditions and has contributed substantially to the current understanding of B. burgdorferi transcriptional regulation. In this review, we present a summary of these studies with particular focus on those that helped define the roles of transcriptional regulators in modulating gene expression in the tick and mammalian milieus. By performing comparative analysis of results derived from the published microarray expression profiling studies, we identified composite gene lists comprising differentially expressed genes in these two environments. Further, we explored the overlap between the regulatory circuits that function during the tick and mammalian phases of the enzootic cycle. Taken together, the data indicate that there is interplay among the distinct signaling pathways that function in feeding ticks and during adaptation to growth in the mammal.

Microarrays doi: 10.3390/microarrays5020008

Authors: Peter Röttgermann Kenneth Dawson Joachim Rädler

Cell fate decisions like apoptosis are heterogeneously implemented within a cell population and, consequently, the population response is recognized as sum of many individual dynamic events. Here, we report on the use of micro-patterned single-cell arrays for real-time tracking of nanoparticle-induced (NP) cell death in sets of thousands of cells in parallel. Annexin (pSIVA) and propidium iodide (PI), two fluorescent indicators of apoptosis, are simultaneously monitored after exposure to functionalized polystyrene (PS − NH 2) nanobeads as a model system. We find that the distribution of Annexin onset times shifts to later times and broadens as a function of decreasing NP dose. We discuss the mean time-to-death as a function of dose, and show how the EC 50 value depends both on dose and time of measurement. In addition, the correlations between the early and late apoptotic markers indicate a systematic shift from apoptotic towards necrotic cell death during the course of the experiment. Thus, our work demonstrates the potential of array-based single cell cytometry for kinetic analysis of signaling cascades in a high-throughput format.

Microarrays doi: 10.3390/microarrays5020007

Authors: Josep Comeron Jordan Reed Matthew Christie Julia Jacobs Jason Dierdorff Daniel Eberl J. Manak

Accurate and rapid identification or confirmation of single nucleotide polymorphisms (SNPs), point mutations and other human genomic variation facilitates understanding the genetic basis of disease. We have developed a new methodology (called MENA (Mismatch EndoNuclease Array)) pairing DNA mismatch endonuclease enzymology with tiling microarray hybridization in order to genotype both known point mutations (such as SNPs) as well as identify previously undiscovered point mutations and small indels. We show that our assay can rapidly genotype known SNPs in a human genomic DNA sample with 99% accuracy, in addition to identifying novel point mutations and small indels with a false discovery rate as low as 10%. Our technology provides a platform for a variety of applications, including: (1) genotyping known SNPs as well as confirming newly discovered SNPs from whole genome sequencing analyses; (2) identifying novel point mutations and indels in any genomic region from any organism for which genome sequence information is available; and (3) screening panels of genes associated with particular diseases and disorders in patient samples to identify causative mutations. As a proof of principle for using MENA to discover novel mutations, we report identification of a novel allele of the beethoven (btv) gene in Drosophila, which encodes a ciliary cytoplasmic dynein motor protein important for auditory mechanosensation.

Microarrays doi: 10.3390/microarrays5010006

Authors: Ling Morgan Brandi Rollins Adolfo Sequeira William Byerley Lynn DeLisi Alan Schatzberg Jack Barchas Richard Myers Stanley Watson Huda Akil William Bunney Marquis Vawter

Genome-wide association studies of schizophrenia encompassing the major histocompatibility locus (MHC) were highly significant following genome-wide correction. This broad region implicates many genes including the MHC complex class II. Within this interval we examined the expression of two MHC II genes (HLA-DPA1 and HLA-DRB1) in brain from individual subjects with schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and controls by differential gene expression methods. A third MHC II mRNA, CD74, was studied outside of the MHC II locus, as it interacts within the same immune complex. Exon microarrays were performed in anterior cingulate cortex (ACC) in BD compared to controls, and both HLA-DPA1 and CD74 were decreased in expression in BD. The expression of HLA-DPA1 and CD74 were both reduced in hippocampus, amygdala, and dorsolateral prefrontal cortex regions in SZ and BD compared to controls by specific qPCR assay. We found several novel HLA-DPA1 mRNA variants spanning HLA-DPA1 exons 2-3-4 as suggested by exon microarrays. The intronic rs9277341 SNP was a significant cis expression quantitative trait locus (eQTL) that was associated with the total expression of HLA-DPA1 in five brain regions. A biomarker study of MHC II mRNAs was conducted in SZ, BD, MDD, and control lymphoblastic cell lines (LCL) by qPCR assay of 87 subjects. There was significantly decreased expression of HLA-DPA1 and CD74 in BD, and trends for reductions in SZ in LCLs. The discovery of multiple splicing variants in brain for HLA-DPA1 is important as the HLA-DPA1 gene is highly conserved, there are no reported splicing variants, and the functions in brain are unknown. Future work on the function and localization of MHC Class II proteins in brain will help to understand the role of alterations in neuropsychiatric disorders. The HLA-DPA1 eQTL is located within a large linkage disequilibrium block that has an irrefutable association with schizophrenia. Future tests in a larger cohort are needed to determine the significance of this eQTL association with schizophrenia. Our findings support the long-held hypothesis that alterations in immune function are associated with the pathophysiology of psychiatric disorders.

Microarrays doi: 10.3390/microarrays5010005

Authors: Michaela Haider Thomas Haselgrübler Alois Sonnleitner Fritz Aberger Jan Hesse

A double-hybridization approach was developed for the enzyme-free detection of specific mRNA of a housekeeping gene. Targeted mRNA was immobilized by hybridization to complementary DNA capture probes spotted onto a microarray. A second hybridization step of Cy5-conjugated label DNA to another section of the mRNA enabled specific labeling of the target. Thus, enzymatic artifacts could be avoided by omitting transcription and amplification steps. This manuscript describes the development of capture probe molecules used in the transcription- and amplification-free analysis of RPLP0 mRNA in isolated total RNA. An increase in specific signal was found with increasing length of the target-specific section of capture probes. Unspecific signal comprising spot autofluorescence and unspecific label binding did not correlate with the capture length. An additional spacer between the specific part of the capture probe and the substrate attachment site increased the signal significantly only on a short capture probe of approximately 30 nt length.

Microarrays doi: 10.3390/microarrays5010004

Authors: Microarrays Editorial Office

The editors of Microarrays would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2015. [...]

Microarrays doi: 10.3390/microarrays5010003

Authors: Tania Puvirajesinghe Jeremy. Turnbull

Microarray technologies inspired the development of carbohydrate arrays. Initially, carbohydrate array technology was hindered by the complex structures of glycans and their structural variability. The first designs of glycoarrays focused on the HTP (high throughput) study of protein–glycan binding events, and subsequently more in-depth kinetic analysis of carbohydrate–protein interactions. However, the applications have rapidly expanded and now achieve successful discrimination of selective interactions between carbohydrates and, not only proteins, but also viruses, bacteria and eukaryotic cells, and most recently even live cell responses to immobilized glycans. Combining array technology with other HTP technologies such as mass spectrometry is expected to allow even more accurate and sensitive analysis. This review provides a broad overview of established glycoarray technologies (with a special focus on glycosaminoglycan applications) and their emerging applications to the study of complex interactions between glycans and whole living cells.

Microarrays doi: 10.3390/microarrays5010002

Authors: Catherine Li Katie Angione Jeff Milunsky

Split hand/foot malformation (SHFM) is a limb malformation with underdeveloped or absent central digital rays, clefts of hands and feet, and variable syndactyly of the remaining digits. There are six types of SHFM. Here, we report a boy with SHFM type 3 having normal 4th and 5th digits, absent 2nd and 3rd digits, and a 4th finger flexion deformity, as well as absent 2nd, 3rd and 4th toes bilaterally. His father, two paternal uncles, and two paternal first cousins have similar phenotype. Chromosome analysis showed a normal male karyotype. A 514 kb gain at 10q24.31–q24.32 (chr10:102,962,134–103,476,346, hg19) was identified using 6.0 Single nucleotide polymorphism (SNP) microarray, resulting in the duplication of nine genes, including BTRC and FBXW4. A detailed systematic review of literature and mapping of breakpoints using microarray data from all reported cases in PubMed and DECIPHER were conducted, and exon 1 of BTRC gene was identified as the critical region responsible for the SHFM3 phenotype. The potential mechanism and future studies of this critical region causing the SHFM3 phenotype are discussed.

Microarrays doi: 10.3390/microarrays5010001

Authors: Jinming Song Haipeng Shao

Cytogenetic analysis is essential for the diagnosis and prognosis of hematopoietic neoplasms in current clinical practice. Many hematopoietic malignancies are characterized by structural chromosomal abnormalities such as specific translocations, inversions, deletions and/or numerical abnormalities that can be identified by karyotype analysis or fluorescence in situ hybridization (FISH) studies. Single nucleotide polymorphism (SNP) arrays offer high-resolution identification of copy number variants (CNVs) and acquired copy-neutral loss of heterozygosity (LOH)/uniparental disomy (UPD) that are usually not identifiable by conventional cytogenetic analysis and FISH studies. As a result, SNP arrays have been increasingly applied to hematopoietic neoplasms to search for clinically-significant genetic abnormalities. A large numbers of CNVs and UPDs have been identified in a variety of hematopoietic neoplasms. CNVs detected by SNP array in some hematopoietic neoplasms are of prognostic significance. A few specific genes in the affected regions have been implicated in the pathogenesis and may be the targets for specific therapeutic agents in the future. In this review, we summarize the current findings of application of SNP arrays in a variety of hematopoietic malignancies with an emphasis on the clinically significant genetic variants.

Microarrays doi: 10.3390/microarrays4040690

Authors: Andrea Flannery Jared Gerlach Lokesh Joshi Michelle Kilcoyne

Carbohydrates play a crucial role in host-microorganism interactions and many host glycoconjugates are receptors or co-receptors for microbial binding. Host glycosylation varies with species and location in the body, and this contributes to species specificity and tropism of commensal and pathogenic bacteria. Additionally, bacterial glycosylation is often the first bacterial molecular species encountered and responded to by the host system. Accordingly, characterising and identifying the exact structures involved in these critical interactions is an important priority in deciphering microbial pathogenesis. Carbohydrate-based microarray platforms have been an underused tool for screening bacterial interactions with specific carbohydrate structures, but they are growing in popularity in recent years. In this review, we discuss carbohydrate-based microarrays that have been profiled with whole bacteria, recombinantly expressed adhesins or serum antibodies. Three main types of carbohydrate-based microarray platform are considered; (i) conventional carbohydrate or glycan microarrays; (ii) whole mucin microarrays; and (iii) microarrays constructed from bacterial polysaccharides or their components. Determining the nature of the interactions between bacteria and host can help clarify the molecular mechanisms of carbohydrate-mediated interactions in microbial pathogenesis, infectious disease and host immune response and may lead to new strategies to boost therapeutic treatments.

Microarrays doi: 10.3390/microarrays4040671

Authors: Samuel Chao Changming Cheng Choong-Chin Liew

Background: Blood has advantages over tissue samples as a diagnostic tool, and blood mRNA transcriptomics is an exciting research field. To realize the full potential of blood transcriptomic investigations requires improved methods for gene expression measurement and data interpretation able to detect biological signatures within the “noisy” variability of whole blood. Methods: We demonstrate collection tube bias compensation during the process of identifying a liver cancer-specific gene signature. The candidate probe set list of liver cancer was filtered, based on previous repeatability performance obtained from technical replicates. We built a prediction model using differential pairs to reduce the impact of confounding factors. We compared prediction performance on an independent test set against prediction on an alternative model derived by Weka. The method was applied to an independent set of 157 blood samples collected in PAXgene tubes. Results: The model discriminated liver cancer equally well in both EDTA and PAXgene collected samples, whereas the Weka-derived model (using default settings) was not able to compensate for collection tube bias. Cross-validation results show our procedure predicted membership of each sample within the disease groups and healthy controls. Conclusion: Our versatile method for blood transcriptomic investigation overcomes several limitations hampering research in blood-based gene tests.

Microarrays doi: 10.3390/microarrays4040647

Authors: Ioannis Valavanis Eleftherios Pilalis Panagiotis Georgiadis Soterios Kyrtopoulos Aristotelis Chatziioannou

DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies.

Microarrays doi: 10.3390/microarrays4040630

Authors: Ana Barat Heather Ruskin Annette Byrne Jochen Prehn

Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype.

Microarrays doi: 10.3390/microarrays4040618

Authors: Denong Wang

This report describes an experimental procedure for constructing integrated lipid, carbohydrate, and protein microarrays. In essence, it prints liposomes on nitrocellulose-coated micro-glass slides, a biochip substrate for spotting protein and carbohydrate microarrays, and the substances that can form liposomes (homo-liposomes) or can be incorporated into liposomes (hetero-liposomes) are suitable for microarray construction using existing microarray spotting devices. Importantly, this technology allows simultaneous detection of serum antibody activities among the three major classes of antigens, i.e., lipids, carbohydrates, and proteins. The potential of this technology is illustrated by its use in revealing a broad-spectrum of pre-existing anti-lipid antibodies in blood circulation and monitoring the epitope spreading of autoantibody reactivities among protein, carbohydrate, and lipid antigens in experimental autoimmune encephalomyelitis (EAE).

Microarrays doi: 10.3390/microarrays4040596

Authors: Xu Wang Mustafa Alshawaqfeh Xuan Dang Bilal Wajid Amina Noor Marwa Qaraqe Erchin Serpedin

In systems biology, the regulation of gene expressions involves a complex network of regulators. Transcription factors (TFs) represent an important component of this network: they are proteins that control which genes are turned on or off in the genome by binding to specific DNA sequences. Transcription regulatory networks (TRNs) describe gene expressions as a function of regulatory inputs specified by interactions between proteins and DNA. A complete understanding of TRNs helps to predict a variety of biological processes and to diagnose, characterize and eventually develop more efficient therapies. Recent advances in biological high-throughput technologies, such as DNA microarray data and next-generation sequence (NGS) data, have made the inference of transcription factor activities (TFAs) and TF-gene regulations possible. Network component analysis (NCA) represents an efficient computational framework for TRN inference from the information provided by microarrays, ChIP-on-chip and the prior information about TF-gene regulation. However, NCA suffers from several shortcomings. Recently, several algorithms based on the NCA framework have been proposed to overcome these shortcomings. This paper first overviews the computational principles behind NCA, and then, it surveys the state-of-the-art NCA-based algorithms proposed in the literature for TRN reconstruction.

Microarrays doi: 10.3390/microarrays4040570

Authors: Chao-Wei Huang Yu-Tsung Lin Shih-Torng Ding Ling-Ling Lo Pei-Hwa Wang En-Chung Lin Fang-Wei Liu Yen-Wen Lu

The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the evaluation time and cost. Compared to traditional approaches, SNP genotyping techniques incorporate informative genetic background, improve the breeding prediction accuracy and acquiesce breeding quality on the farm. This article therefore reviews the typical procedures of animal breeding using SNPs and the current status of related techniques. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, along with their potential are highlighted. Examples in pig and poultry with different SNP loci linked to high economic trait values are given. The recommendations for utilizing SNP genotyping in nimal breeding are summarized.

Microarrays doi: 10.3390/microarrays4040551

Authors: Maryam Etebari Mohsen Navari Pier Piccaluga

The traditional methods for detection of chromosomal aberrations, which included cytogenetic or gene candidate solutions, suffered from low sensitivity or the need for previous knowledge of the target regions of the genome. With the advent of single nucleotide polymorphism (SNP) arrays, genome screening at global level in order to find chromosomal aberrations like copy number variants, DNA amplifications, deletions, and also loss of heterozygosity became feasible. In this review, we present an update of the knowledge, gained by SNPs arrays, of the genomic complexity of the most important subtypes of non-Hodgkin lymphomas.

Microarrays doi: 10.3390/microarrays4040540

Authors: Michael Januszyk Robert Rennert Michael Sorkin Zeshaan Maan Lisa Wong Alexander Whittam Arnetha Whitmore Dominik Duscher Geoffrey Gurtner

Significant transcriptional heterogeneity is an inherent property of complex tissues such as tumors and healing wounds. Traditional methods of high-throughput analysis rely on pooling gene expression data from hundreds of thousands of cells and reporting a population-wide average that is unable to capture differences within distinct cell subsets. Recent advances in microfluidic technology have permitted the development of large-scale single cell analytic methods that overcome this limitation. The increased granularity afforded by such approaches allows us to answer the critical question of whether expansion in cell culture significantly alters the transcriptional characteristics of cells isolated from primary tissue. Here we examine an established population of human adipose-derived stem cells (ASCs) using a novel, microfluidic-based method for high-throughput transcriptional interrogation, coupled with advanced bioinformatic analysis, to evaluate the dynamics of single cell gene expression among primary, passage 0, and passage 1 stem cells. We find significant differences in the transcriptional profiles of cells from each group, as well as a considerable shift in subpopulation dynamics as those subgroups better able to adhere and proliferate under these culture conditions gradually emerge as dominant. Taken together, these findings reinforce the importance of using primary or very early passage cells in future studies.

Microarrays doi: 10.3390/microarrays4040520

Authors: Astrid Wachter Stephan Bernhardt Tim Beissbarth Ulrike Korf

Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.

Microarrays doi: 10.3390/microarrays4040503

Authors: Aimy Sebastian Nicholas Hum Bryan Hudson Gabriela Loots

Dynamic interaction between prostate cancer and the bone microenvironment is a major contributor to metastasis of prostate cancer to bone. In this study, we utilized an in vitro co-culture model of PC3 prostate cancer cells and osteoblasts followed by microarray based gene expression profiling to identify previously unrecognized prostate cancer–bone microenvironment interactions. Factors secreted by PC3 cells resulted in the up-regulation of many genes in osteoblasts associated with bone metabolism and cancer metastasis, including Mmp13, Il-6 and Tgfb2, and down-regulation of Wnt inhibitor Sost. To determine whether altered Sost expression in the bone microenvironment has an effect on prostate cancer metastasis, we co-cultured PC3 cells with Sost knockout (SostKO) osteoblasts and wildtype (WT) osteoblasts and identified several genes differentially regulated between PC3-SostKO osteoblast co-cultures and PC3-WT osteoblast co-cultures. Co-culturing PC3 cells with WT osteoblasts up-regulated cancer-associated long noncoding RNA (lncRNA) MALAT1 in PC3 cells. MALAT1 expression was further enhanced when PC3 cells were co-cultured with SostKO osteoblasts and treatment with recombinant Sost down-regulated MALAT1 expression in these cells. Our results suggest that reduced Sost expression in the tumor microenvironment may promote bone metastasis by up-regulating MALAT1 in prostate cancer.

Microarrays doi: 10.3390/microarrays4040490

Authors: Sarah Nickerson Renate Marquis-Nicholson Karen Claxton Fern Ashton Ivone Leong Debra Prosser Jennifer Love Alice George Graham Taylor Callum Wilson R. Gardner Donald Love

Autosomal recessive cerebellar ataxia encompasses a large and heterogeneous group of neurodegenerative disorders. We employed single nucleotide polymorphism (SNP) analysis and whole exome sequencing to investigate a consanguineous Maori pedigree segregating ataxia. We identified a novel mutation in exon 10 of the SACS gene: c.7962T>G p.(Tyr2654*), establishing the diagnosis of autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS). Our findings expand both the genetic and phenotypic spectrum of this rare disorder, and highlight the value of high-density SNP analysis and whole exome sequencing as powerful and cost-effective tools in the diagnosis of genetically heterogeneous disorders such as the hereditary ataxias.

Microarrays doi: 10.3390/microarrays4040474

Authors: Michael Mauk Changchun Liu Jinzhao Song Haim Bau

Microfluidic components and systems for rapid (<60 min), low-cost, convenient, field-deployable sequence-specific nucleic acid-based amplification tests (NAATs) are described. A microfluidic point-of-care (POC) diagnostics test to quantify HIV viral load from blood samples serves as a representative and instructive example to discuss the technical issues and capabilities of “lab on a chip” NAAT devices. A portable, miniaturized POC NAAT with performance comparable to conventional PCR (polymerase-chain reaction)-based tests in clinical laboratories can be realized with a disposable, palm-sized, plastic microfluidic chip in which: (1) nucleic acids (NAs) are extracted from relatively large (~mL) volume sample lysates using an embedded porous silica glass fiber or cellulose binding phase (“membrane”) to capture sample NAs in a flow-through, filtration mode; (2) NAs captured on the membrane are isothermally (~65 °C) amplified; (3) amplicon production is monitored by real-time fluorescence detection, such as with a smartphone CCD camera serving as a low-cost detector; and (4) paraffin-encapsulated, lyophilized reagents for temperature-activated release are pre-stored in the chip. Limits of Detection (LOD) better than 103 virons/sample can be achieved. A modified chip with conduits hosting a diffusion-mode amplification process provides a simple visual indicator to readily quantify sample NA template. In addition, a companion microfluidic device for extracting plasma from whole blood without a centrifuge, generating cell-free plasma for chip-based molecular diagnostics, is described. Extensions to a myriad of related applications including, for example, food testing, cancer screening, and insect genotyping are briefly surveyed.

Microarrays doi: 10.3390/microarrays4040454

Authors: Kenichi Inaoka Yoshikuni Inokawa Shuji Nomoto

DNA microarray technologies have advanced rapidly and had a profound impact on examining gene expression on a genomic scale in research. This review discusses the history and development of microarray and DNA chip devices, and specific microarrays are described along with their methods and applications. In particular, microarrays have detected many novel cancer-related genes by comparing cancer tissues and non-cancerous tissues in oncological research. Recently, new methods have been in development, such as the double-combination array and triple-combination array, which allow more effective analysis of gene expression and epigenetic changes. Analysis of gene expression alterations in precancerous regions compared with normal regions and array analysis in drug-resistance cancer tissues are also successfully performed. Compared with next-generation sequencing, a similar method of genome analysis, several important differences distinguish these techniques and their applications. Development of novel microarray technologies is expected to contribute to further cancer research.

Microarrays doi: 10.3390/microarrays4040432

Authors: Farhad Maleki Anthony Kusalik

Cellular pathways involve the phosphorylation and dephosphorylation of proteins. Peptide microarrays called kinome arrays facilitate the measurement of the phosphorylation activity of hundreds of proteins in a single experiment. Analyzing the data from kinome microarrays is a multi-step process. Typically, various techniques are possible for a particular step, and it is necessary to compare and evaluate them. Such evaluations require data for which correct analysis results are known. Unfortunately, such kinome data is not readily available in the community. Further, there are no established techniques for creating artificial kinome datasets with known results and with the same characteristics as real kinome datasets. In this paper, a methodology for generating synthetic kinome array data is proposed. The methodology relies on actual intensity measurements from kinome microarray experiments and preserves their subtle characteristics. The utility of the methodology is demonstrated by evaluating methods for eliminating heterogeneous variance in kinome microarray data. Phosphorylation intensities from kinome microarrays often exhibit such heterogeneous variance and its presence can negatively impact downstream statistical techniques that rely on homogeneity of variance. It is shown that using the output from the proposed synthetic data generator, it is possible to critically compare two variance stabilization methods.

Microarrays doi: 10.3390/microarrays4030424

Authors: Dania Albaba Sanam Soomro Chandra Mohan

In recent years, aptamers have come to replace antibodies in high throughput multiplexed experiments. The aptamer-based biomarker screening technology, which kicked off in 2010, is capable of interrogating thousands of proteins in a very small sample volume. With this new technology, researchers hope to find clinically appropriate biomarkers for a myriad of illnesses by screening human body fluids. In this work, we have reviewed a total of eight studies utilizing aptamer-based biomarker screens of human body fluids, and have highlighted novel protein biomarkers discovered.

Microarrays doi: 10.3390/microarrays4030407

Authors: Logan Walker George Wiggins John Pearson

Constitutional copy number variants (CNVs) include inherited and de novo deviations from a diploid state at a defined genomic region. These variants contribute significantly to genetic variation and disease in humans, including breast cancer susceptibility. Identification of genetic risk factors for breast cancer in recent years has been dominated by the use of genome-wide technologies, such as single nucleotide polymorphism (SNP)-arrays, with a significant focus on single nucleotide variants. To date, these large datasets have been underutilised for generating genome-wide CNV profiles despite offering a massive resource for assessing the contribution of these structural variants to breast cancer risk. Technical challenges remain in determining the location and distribution of CNVs across the human genome due to the accuracy of computational prediction algorithms and resolution of the array data. Moreover, better methods are required for interpreting the functional effect of newly discovered CNVs. In this review, we explore current and future application of SNP array technology to assess rare and common CNVs in association with breast cancer risk in humans.

Microarrays doi: 10.3390/microarrays4030389

Authors: Christopher Walsh Pingzhao Hu Jane Batt Claudia Santos

The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers.

Microarrays doi: 10.3390/microarrays4030370

Authors: Sarah Picaud Panagis Filippakopoulos

Post translational modifications have been recognized as chemical signals that create docking sites for evolutionary conserved effector modules, allowing for signal integration within large networks of interactions. Lysine acetylation in particular has attracted attention as a regulatory modification, affecting chromatin structure and linking to transcriptional activation. Advances in peptide array technologies have facilitated the study of acetyl-lysine-containing linear motifs interacting with the evolutionary conserved bromodomain module, which specifically recognizes and binds to acetylated sequences in histones and other proteins. Here we summarize recent work employing SPOT peptide technology to identify acetyl-lysine dependent interactions and document the protocols adapted in our lab, as well as our efforts to characterize such bromodomain-histone interactions. Our results highlight the versatility of SPOT methods and establish an affordable tool for rapid access to potential protein/modified-peptide interactions involving lysine acetylation.

Microarrays doi: 10.3390/microarrays4030339

Authors: Javier Arsuaga Tyler Borrman Raymond Cavalcante Georgina Gonzalez Catherine Park

DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.

Microarrays doi: 10.3390/microarrays4030324

Authors: Clare Coveney David Boocock Robert Rees Suha Deen Graham Ball

The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two carefully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy.

Microarrays doi: 10.3390/microarrays4030311

Authors: Manish Biyani Takanori Ichiki

Advances in lithographic approaches to fabricating bio-microarrays have been extensively explored over the last two decades. However, the need for pattern flexibility, a high density, a high resolution, affordability and on-demand fabrication is promoting the development of unconventional routes for microarray fabrication. This review highlights the development and uses of a new molecular lithography approach, called “microintaglio printing technology”, for large-scale bio-microarray fabrication using a microreactor array (µRA)-based chip consisting of uniformly-arranged, femtoliter-size µRA molds. In this method, a single-molecule-amplified DNA microarray pattern is self-assembled onto a µRA mold and subsequently converted into a messenger RNA or protein microarray pattern by simultaneously producing and transferring (immobilizing) a messenger RNA or a protein from a µRA mold to a glass surface. Microintaglio printing allows the self-assembly and patterning of in situ-synthesized biomolecules into high-density (kilo-giga-density), ordered arrays on a chip surface with µm-order precision. This holistic aim, which is difficult to achieve using conventional printing and microarray approaches, is expected to revolutionize and reshape proteomics. This review is not written comprehensively, but rather substantively, highlighting the versatility of microintaglio printing for developing a prerequisite platform for microarray technology for the postgenomic era.

Microarrays doi: 10.3390/microarrays4020287

Authors: Enery Lorenzo Katia Camacho-Caceres Alexander Ropelewski Juan Rosas Michael Ortiz-Mojer Lynn Perez-Marty Juan Irizarry Valerie Gonzalez Jesús Rodríguez Mauricio Cabrera-Rios Clara Isaza

Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High‑throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path.

Microarrays doi: 10.3390/microarrays4020270

Authors: Jeannette Koschmann Anirban Bhar Philip Stegmaier Alexander Kel Edgar Wingender

A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowledge-based analysis of the upstream pathway that control the activity of these TFs is shown to lead to hypothetical master regulators. This strategy was implemented as a workflow in a comprehensive bioinformatic software platform. We applied this workflow to gene sets that were identified by a novel triclustering algorithm in naphthalene-induced gene expression signatures of murine liver and lung tissue. As a result, tissue-specific master regulators were identified that are known to be linked with tumorigenic and apoptotic processes. To our knowledge, this is the first time that genes of expression triclusters were used to identify upstream regulators.

Microarrays doi: 10.3390/microarrays4020255

Authors: Alina Sîrbu Martin Crane Heather Ruskin

Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

Microarrays doi: 10.3390/microarrays4020245

Authors: Aurélien Lacombe Vincenza Carafa Sandra Schneider Melanie Sticker-Jantscheff Luigi Tornillo Serenella Eppenberger-Castori

Tissue microarray (TMA) methodology allows the concomitant analysis of hundreds of tissue specimens arrayed in the same manner on a recipient block. Subsequently, all samples can be processed under identical conditions, such as antigen retrieval procedure, reagent concentrations, incubation times with antibodies/probes, and escaping the inter-assays variability. Therefore, the use of TMA has revolutionized histopathology translational research projects and has become a tool very often used for putative biomarker investigations. TMAs are particularly relevant for large scale analysis of a defined disease entity. In the course of these exploratory studies, rare subpopulations can be discovered or identified. This can refer to subsets of patients with more particular phenotypic or genotypic disease with low incidence or to patients receiving a particular treatment. Such rare cohorts should be collected for more specific investigations at a later time, when, possibly, more samples of a rare identity will be available as well as more knowledge derived from concomitant, e.g., genetic, investigations will have been acquired. In this article we analyze for the first time the limits and opportunities to construct new TMA blocks using tissues from older available arrays and supplementary donor blocks. In summary, we describe the reasons and technical details for the construction of rare disease entities arrays.

Microarrays doi: 10.3390/microarrays4020228

Authors: Amir Syahir Kenji Usui Kin-ya Tomizaki Kotaro Kajikawa Hisakazu Mihara

Protein microarray technology has gone through numerous innovative developments in recent decades. In this review, we focus on the development of protein detection methods embedded in the technology. Early microarrays utilized useful chromophores and versatile biochemical techniques dominated by high-throughput illumination. Recently, the realization of label-free techniques has been greatly advanced by the combination of knowledge in material sciences, computational design and nanofabrication. These rapidly advancing techniques aim to provide data without the intervention of label molecules. Here, we present a brief overview of this remarkable innovation from the perspectives of label and label-free techniques in transducing nano‑biological events.

Microarrays doi: 10.3390/microarrays4020214

Authors: Paula Díez María González-González Lucía Lourido Rosa Dégano Nieves Ibarrola Juan Casado-Vela Joshua LaBaer Manuel Fuentes

Nucleic Acid Programmable Protein Arrays (NAPPA) have emerged as a powerful and innovative technology for the screening of biomarkers and the study of protein-protein interactions, among others possible applications. The principal advantages are the high specificity and sensitivity that this platform offers. Moreover, compared to conventional protein microarrays, NAPPA technology avoids the necessity of protein purification, which is expensive and time-consuming, by substituting expression in situ with an in vitro transcription/translation kit. In summary, NAPPA arrays have been broadly employed in different studies improving knowledge about diseases and responses to treatments. Here, we review the principal advances and applications performed using this platform during the last years.

Microarrays doi: 10.3390/microarrays4020196

Authors: Sarah Schumacher Sandra Muekusch Harald Seitz

This review addresses up-to-date applications of Protein Microarrays. Protein Microarrays play a significant role in basic research as well as in clinical applications and are applicable in a lot of fields, e.g., DNA, proteins and small molecules. Additionally they are on the way to enter clinics in routine diagnostics. Protein Microarrays can be powerful tools to improve healthcare. An overview of basic characteristics to mediate essential knowledge of this technique is given. To reach this goal, some challenges still have to be addressed. A few applications of Protein Microarrays in a medical context are shown. Finally, an outlook, where the potential of Protein Microarrays is depicted and speculations how the future of Protein Microarrays will look like are made.

Microarrays doi: 10.3390/microarrays4020188

Authors: Erik Vassella José Galván Inti Zlobec

Background: Tissue microarray (TMA) technology allows rapid visualization of molecular markers by immunohistochemistry and in situ hybridization. In addition, TMA instrumentation has the potential to assist in other applications: punches taken from donor blocks can be placed directly into tubes and used for nucleic acid analysis by PCR approaches. However, the question of possible cross-contamination between samples punched with the same device has frequently been raised but never addressed. Methods: Two experiments were performed. (1) A block from mycobacterium tuberculosis (TB) positive tissue and a second from an uninfected patient were aligned side-by-side in an automated tissue microarrayer. Four 0.6 mm punches were cored from each sample and placed inside their corresponding tube. Between coring of each donor block, a mechanical cleaning step was performed by insertion of the puncher into a paraffin block. This sequence of coring and cleaning was repeated three times, alternating between positive and negative blocks. A fragment from the 6110 insertion sequence specific for mycobacterium tuberculosis was analyzed; (2) Four 0.6 mm punches were cored from three KRAS mutated colorectal cancer blocks, alternating with three different wild-type tissues using the same TMA instrument (sequence of coring: G12D, WT, G12V, WT, G13D and WT). Mechanical cleaning of the device between each donor block was made. Mutation analysis by pyrosequencing was carried out. This sequence of coring was repeated manually without any cleaning step between blocks. Results/Discussion: In both analyses, all alternating samples showed the expected result (samples 1, 3 and 5: positive or mutated, samples 2, 4 and 6: negative or wild-type). Similar results were obtained without cleaning step. These findings suggest that no cross-contamination of tissue samples occurs when donor blocks are punched using the same device, however a cleaning step is nonetheless recommended. Our result supports the use of TMA technology as an accessory to PCR applications.

Microarrays doi: 10.3390/microarrays4020162

Authors: Stefanie Brezina Regina Soldo Roman Kreuzhuber Philipp Hofer Andrea Gsur Andreas Weinhaeusel

New minimal invasive diagnostic methods for early detection of lung cancer are urgently needed. It is known that the immune system responds to tumors with production of tumor-autoantibodies. Protein microarrays are a suitable highly multiplexed platform for identification of autoantibody signatures against tumor-associated antigens (TAA). These microarrays can be probed using 0.1 mg immunoglobulin G (IgG), purified from 10 µL of plasma. We used a microarray comprising recombinant proteins derived from 15,417 cDNA clones for the screening of 100 lung cancer samples, including 25 samples of each main histological entity of lung cancer, and 100 controls. Since this number of samples cannot be processed at once, the resulting data showed non-biological variances due to “batch effects”. Our aim was to evaluate quantile normalization, “distance-weighted discrimination” (DWD), and “ComBat” for their effectiveness in data pre-processing for elucidating diagnostic immune‑signatures. “ComBat” data adjustment outperformed the other methods and allowed us to identify classifiers for all lung cancer cases versus controls and small-cell, squamous cell, large-cell, and adenocarcinoma of the lung with an accuracy of 85%, 94%, 96%, 92%, and 83% (sensitivity of 0.85, 0.92, 0.96, 0.88, 0.83; specificity of 0.85, 0.96, 0.96, 0.96, 0.83), respectively. These promising data would be the basis for further validation using targeted autoantibody tests.

Microarrays doi: 10.3390/microarrays4020133

Authors: Therese Andersen Pia Auk-Emblem Michael Dornish

This review compiles information regarding the use of alginate, and in particular alginate hydrogels, in culturing cells in 3D. Knowledge of alginate chemical structure and functionality are shown to be important parameters in design of alginate-based matrices for cell culture. Gel elasticity as well as hydrogel stability can be impacted by the type of alginate used, its concentration, the choice of gelation technique (ionic or covalent), and divalent cation chosen as the gel inducing ion. The use of peptide-coupled alginate can control cell–matrix interactions. Gelation of alginate with concomitant immobilization of cells can take various forms. Droplets or beads have been utilized since the 1980s for immobilizing cells. Newer matrices such as macroporous scaffolds are now entering the 3D cell culture product market. Finally, delayed gelling, injectable, alginate systems show utility in the translation of in vitro cell culture to in vivo tissue engineering applications. Alginate has a history and a future in 3D cell culture. Historically, cells were encapsulated in alginate droplets cross-linked with calcium for the development of artificial organs. Now, several commercial products based on alginate are being used as 3D cell culture systems that also demonstrate the possibility of replacing or regenerating tissue.

Microarrays doi: 10.3390/microarrays4020115

Authors: Martin Witt Johanna-Gabriela Walter Frank Stahl

Microarray technologies are state of the art in biological research, which requires fast genome, proteome and transcriptome analysis technologies. Often antibodies are applied in protein microarrays as proteomic tools. Since the generation of antibodies against toxic targets or small molecules including organic compounds remains challenging the use of antibodies may be limited in this context. In contrast to this, aptamer microarrays provide alternative techniques to circumvent these limitations. In this article we review the latest developments in aptamer microarray technology. We discuss similarities and differences between DNA and aptamer microarrays and shed light on the post synthesis immobilization of aptamers including corresponding effects on the microarray performance. Finally, we highlight current limitations and future prospects of aptamer microarray technology.

Microarrays doi: 10.3390/microarrays4020098

Authors: Stefanie Boellner Karl-Friedrich Becker

Reverse Phase Protein Arrays (RPPA) represent a very promising sensitive and precise high-throughput technology for the quantitative measurement of hundreds of signaling proteins in biological and clinical samples. This array format allows quantification of one protein or phosphoprotein in multiple samples under the same experimental conditions at the same time. Moreover, it is suited for signal transduction profiling of small numbers of cultured cells or cells isolated from human biopsies, including formalin fixed and paraffin embedded (FFPE) tissues. Owing to the much easier sample preparation, as compared to mass spectrometry based technologies, and the extraordinary sensitivity for the detection of low-abundance signaling proteins over a large linear range, RPPA have the potential for characterization of deregulated interconnecting protein pathways and networks in limited amounts of sample material in clinical routine settings. Current aspects of RPPA technology, including dilution curves, spotting, controls, signal detection, antibody validation, and calculation of protein levels are addressed.

Microarrays doi: 10.3390/microarrays4010084

Authors: Hueman Jaimes-Díaz Violeta Larios-Serrato Teresa Lloret-Sánchez Gabriela Olguín-Ruiz Carlos Sánchez-Vallejo Luis Carreño-Durán Rogelio Maldonado-Rodríguez Alfonso Méndez-Tenorio

In this study we evaluate the capacity of Virtual Hybridization to identify between highly related bacterial strains. Eight genomic fingerprints were obtained by virtual hybridization for the Bacillus anthracis genome set, and a set of 15,264 13-nucleotide short probes designed to produce genomic fingerprints unique for each organism. The data obtained from each genomic fingerprint were used to obtain hybridization patterns simulating a DNA microarray. Two virtual hybridization methods were used: the Direct and the Extended method to identify the number of potential hybridization sites and thus determine the minimum sensitivity value to discriminate between genomes with 99.9% similarity. Genomic fingerprints were compared using both methods and phylogenomic trees were constructed to verify that the minimum detection value is 0.000017. Results obtained from the genomic fingerprints suggest that the distribution in the trees is correct, as compared to other taxonomic methods. Specific virtual hybridization sites for each of the genomes studied were also identified.

Microarrays doi: 10.3390/microarrays4010064

Authors: Anastasia Bachmann Matthias Moll Eric Gottwald Cordula Nies Roman Zantl Helga Wagner Britta Burkhardt Juan Sánchez Ruth Ladurner Wolfgang Thasler Georg Damm Andreas Nussler

One of the main challenges in drug development is the prediction of in vivo toxicity based on in vitro data. The standard cultivation system for primary human hepatocytes is based on monolayer cultures, even if it is known that these conditions result in a loss of hepatocyte morphology and of liver-specific functions, such as drug-metabolizing enzymes and transporters. As it has been demonstrated that hepatocytes embedded between two sheets of collagen maintain their function, various hydrogels and scaffolds for the 3D cultivation of hepatocytes have been developed. To further improve or maintain hepatic functions, 3D cultivation has been combined with perfusion. In this manuscript, we discuss the benefits and drawbacks of different 3D microfluidic devices. For most systems that are currently available, the main issues are the requirement of large cell numbers, the low throughput, and expensive equipment, which render these devices unattractive for research and the drug-developing industry. A higher acceptance of these devices could be achieved by their simplification and their compatibility with high-throughput, as both aspects are of major importance for a user-friendly device.

Microarrays doi: 10.3390/microarrays4010053

Authors: Jeong Lee Kai Bao John Frangioni Hak Choi

The screening of living cells using high-throughput microarrays is technically challenging. Great care must be taken in the chemical presentation of potential ligands and the number of collisions that cells make with them. To overcome these issues, we have developed a glass slide-based microarray system to discover small molecule ligands that preferentially bind to one cell type over another, including when the cells differ by only a single receptor. Chemical spots of 300 ± 10 µm in diameter are conjugated covalently to glass slides using an arraying robot, and novel near-infrared fluorophores with peak emission at 700 nm and 800 nm are used to label two different cell types. By carefully optimizing incubation conditions, including cell density, motion, kinetics, detection, etc. we demonstrate that cell-ligand binding occurs, and that the number of cells bound per chemical spot correlates with ligand affinity and specificity. This screening system lays the foundation for high-throughput discovery of novel ligands to the cell surface.

Microarrays doi: 10.3390/microarrays4010041

Authors: Feriel Melaine Yoann Roupioz Arnaud Buhot

The detection of small molecules by biosensors remains a challenge for diagnostics in many areas like pharmacology, environment or homeland security. The main difficulty comes from both the low molecular weight and low concentrations of most targets, which generally requires an indirect detection with an amplification or a sandwich procedure. In this study, we combine both strategies as the amplification of Surface Plasmon Resonance imaging (SPRi) signal is obtained by the use of gold nanoparticles and the sequence engineering of split-aptamers, short oligonucleotides strands with strong affinity towards small targets, allows for a sandwich structure. Combining those two strategies, we obtained state-of-the-art results in the limit of detection (LOD = 50 nM) with the model target adenosine. Furthermore, the SPRi detection led on aptamer microarrays paves the way for potential multi-target detections thanks to the multi-probe imaging approach.

Microarrays doi: 10.3390/microarrays4010025

Authors: Junko Takahashi Masaki Misawa Hitoshi Iwahashi

5-Aminolevulinic acid (ALA) is a precursor of the photosensitizer used in photodynamic therapy. It accumulates in tumor cells and subsequently metabolizes to protoporphyrin IX (PpIX), which generates singlet oxygen after light irradiation. PpIX enhances the generation of reactive oxygen species following physicochemical interactions with X-rays. ALA-based treatment using fractionated doses of irradiation suppressed tumor growth in a mouse melanoma model. To study the transcriptomic effects of PpIX, microarray analyses were conducted using HeLa cells with limited proliferation capacity. Based on the p-values (p < 0.01), we selected genes showing altered expression in each treatment group with reference to the non-treatment (NT) group. We detected 290, 196 and 28 upregulated genes, as well as 203, 146 and 36 downregulated genes after a 6 h-long PpIX treatment (1 μg/mL) prior to 3 Gy X-ray irradiation (PpIX-XT), 3 Gy X-ray irradiation alone (XT) and PpIX treatment alone (PpIXT), respectively. Functional analysis revealed that a majority of the regulated genes in the XT and PpIX-XT groups were related to cell-cycle arrest. The XT and PpIX-XT groups differed in the quantity, but not in the quality of their gene expression. The combined effect of PpIX and X-ray irradiation sensitized HeLa cells to X-ray treatment.

Microarrays doi: 10.3390/microarrays4010002

Authors: Motohide Hori Tomoya Nakamachi Junko Shibato Randeep Rakwal Seiji Shioda Satoshi Numazawa

Our group has been systematically investigating the effects of the neuropeptide pituitary adenylate-cyclase activating polypeptide (PACAP) on the ischemic brain. To do so, we have established and utilized the permanent middle cerebral artery occlusion (PMCAO) mouse model, in which PACAP38 (1 pmol) injection is given intracerebroventrically and compared to a control saline (0.9% sodium chloride, NaCl) injection, to unravel genome‑wide gene expression changes using a high-throughput DNA microarray analysis approach. In our previous studies, we have accumulated a large volume of data (gene inventory) from the whole brain (ipsilateral and contralateral hemispheres) after both PMCAO and post-PACAP38 injection. In our latest research, we have targeted specifically infarct or ischemic core (hereafter abbreviated IC) and penumbra (hereafter abbreviated P) post-PACAP38 injections in order to re-examine the transcriptome at 6 and 24 h post injection. The current study aims to delineate the specificity of expression and localization of differentially expressed molecular factors influenced by PACAP38 in the IC and P regions. Utilizing the mouse 4 × 44 K whole genome DNA chip we show numerous changes (≧/≦ 1.5/0.75-fold) at both 6 h (654 and 456, and 522 and 449 up- and down-regulated genes for IC and P, respectively) and 24 h (2568 and 2684, and 1947 and 1592 up- and down-regulated genes for IC and P, respectively) after PACAP38 treatment. Among the gene inventories obtained here, two genes, brain-derived neurotrophic factor (Bdnf) and transthyretin (Ttr) were found to be induced by PACAP38 treatment, which we had not been able to identify previously using the whole hemisphere transcriptome analysis. Using bioinformatics analysis by pathway- or specific-disease-state focused gene classifications and Ingenuity Pathway Analysis (IPA) the differentially expressed genes are functionally classified and discussed. Among these, we specifically discuss some novel and previously identified genes, such as alpha hemoglobin stabilizing protein (Ahsp), cathelicidin antimicrobial peptide (Camp), chemokines, interferon beta 1 (Ifnb1), and interleukin 6 (Il6) in context of PACAP38-mediated neuroprotection in the ischemic brain. Taken together, the DNA microarray analysis provides not only a great resource for further study, but also reinforces the importance of region-specific analyses in genome-wide identification of target molecular factors that might play a role in the neuroprotective function of PACAP38.

Microarrays doi: 10.3390/microarrays4010001

Authors: Microarrays Editorial Office

The editors of Microarrays would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2014:[...]

Microarrays doi: 10.3390/microarrays3040340

Authors: Marcelo Boareto Nestor Caticha

Microarray data analysis typically consists in identifying a list of differentially expressed genes (DEG), i.e., the genes that are differentially expressed between two experimental conditions. Variance shrinkage methods have been considered a better choice than the standard t-test for selecting the DEG because they correct the dependence of the error with the expression level. This dependence is mainly caused by errors in background correction, which more severely affects genes with low expression values. Here, we propose a new method for identifying the DEG that overcomes this issue and does not require background correction or variance shrinkage. Unlike current methods, our methodology is easy to understand and implement. It consists of applying the standard t-test directly on the normalized intensity data, which is possible because the probe intensity is proportional to the gene expression level and because the t-test is scale- and location-invariant. This methodology considerably improves the sensitivity and robustness of the list of DEG when compared with the t-test applied to preprocessed data and to the most widely used shrinkage methods, Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA). Our approach is useful especially when the genes of interest have small differences in expression and therefore get ignored by standard variance shrinkage methods.

Microarrays doi: 10.3390/microarrays3040322

Authors: Mario Fasold Hans Binder

The great utility of microarrays for genome-scale expression analysis is challenged by the widespread presence of batch effects, which bias expression measurements in particular within large data sets. These unwanted technical artifacts can obscure biological variation and thus significantly reduce the reliability of the analysis results. It is largely unknown which are the predominant technical sources leading to batch effects. We here quantitatively assess the prevalence and impact of several known technical effects on microarray expression results. Particularly, we focus on important factors such as RNA degradation, RNA quantity, and sequence biases including multiple guanine effects. We find that the common variation of RNA quality and RNA quantity can not only yield low-quality expression results, but that both factors also correlate with batch effects and biological characteristics of the samples.

Microarrays doi: 10.3390/microarrays3040302

Authors: Niccolò Bassani Federico Ambrogi Elia Biganzoli

Over the last few years, miRNA microarray platforms have provided great insights into the biological mechanisms underlying the onset and development of several diseases. However, only a few studies have evaluated the concordance between different microarray platforms using methods that took into account measurement error in the data. In this work, we propose the use of a modified version of the Bland–Altman plot to assess agreement between microarray platforms. To this aim, two samples, one renal tumor cell line and a pool of 20 different human normal tissues, were profiled using three different miRNA platforms (Affymetrix, Agilent, Illumina) on triplicate arrays. Intra-platform reliability was assessed by calculating pair-wise concordance correlation coefficients (CCC) between technical replicates and overall concordance correlation coefficient (OCCC) with bootstrap percentile confidence intervals, which revealed moderate-to-good repeatability of all platforms for both samples. Modified Bland–Altman analysis revealed good patterns of concordance for Agilent and Illumina, whereas Affymetrix showed poor-to-moderate agreement for both samples considered. The proposed method is useful to assess agreement between array platforms by modifying the original Bland–Altman plot to let it account for measurement error and bias correction and can be used to assess patterns of concordance between other kinds of arrays other than miRNA microarrays.

Microarrays doi: 10.3390/microarrays3040282

Authors: Ingeborg Hospach Yvonne Joseph Michaela Mai Nadejda Krasteva Gabriele Nelles

Cytokine proteins are known as biomarker molecules, characteristic of a disease or specific body condition. Monitoring of the cytokine pattern in body fluids can contribute to the diagnosis of diseases. Here we report on the development of an array comprised of different anti-cytokine antibodies on an activated solid support coupled with a fluorescence readout mechanism. Optimization of the array preparation was done in regard of spot homogeneity and spot size. The proinflammatory cytokines Tumor Necrosis Factor alpha (TNFα) and Interleukin 6 (IL-6) were chosen as the first targets of interest. First, the solid support for covalent antibody immobilization and an adequate fluorescent label were selected. Three differently functionalized glass substrates for spotting were compared: amine and epoxy, both having a two-dimensional structure, and the NHS functionalized hydrogel (NHS-3D). The NHS-hydrogel functionalization of the substrate was best suited to antibody immobilization. Then, the optimization of plotting parameters and geometry as well as buffer media were investigated, considering the ambient analyte theory of Roger Ekins. As a first step towards real sample studies, a proof of principle of cytokine detection has been established.

Microarrays doi: 10.3390/microarrays3040263

Authors: Yick Wong Qi Kwong Heng Lee Chuang Ong Sean Mayes Fook Chew David Appleton Harikrishna Kulaveerasingam

Gene expression changes that occur during mesocarp development are a major research focus in oil palm research due to the economic importance of this tissue and the relatively rapid increase in lipid content to very high levels at fruit ripeness. Here, we report the development of a transcriptome-based 105,000-probe oil palm mesocarp microarray. The expression of genes involved in fatty acid (FA) and triacylglycerol (TAG) assembly, along with the tricarboxylic acid cycle (TCA) and glycolysis pathway at 16 Weeks After Anthesis (WAA) exhibited significantly higher signals compared to those obtained from a cross-species hybridization to the Arabidopsis (p-value < 0.01), and rice (p-value < 0.01) arrays. The oil palm microarray data also showed comparable correlation of expression (r2 = 0.569, p < 0.01) throughout mesocarp development to transcriptome (RNA sequencing) data, and improved correlation over quantitative real-time PCR (qPCR) (r2 = 0.721, p < 0.01) of the same RNA samples. The results confirm the advantage of the custom microarray over commercially available arrays derived from model species. We demonstrate the utility of this custom microarray to gain a better understanding of gene expression patterns in the oil palm mesocarp that may lead to increasing future oil yield.

Microarrays doi: 10.3390/microarrays3040245

Authors: Alexander Nesterov-Mueller Frieder Maerkle Lothar Hahn Tobias Foertsch Sebastian Schillo Valentina Bykovskaya Martyna Sedlmayr Laura Weber Barbara Ridder Miriam Soehindrijo Bastian Muenster Jakob Striffler F. Bischoff Frank Breitling Felix Loeffler

In this review, we describe different methods of microarray fabrication based on the use of micro-particles/-beads and point out future tendencies in the development of particle-based arrays. First, we consider oligonucleotide bead arrays, where each bead is a carrier of one specific sequence of oligonucleotides. This bead-based array approach, appearing in the late 1990s, enabled high-throughput oligonucleotide analysis and had a large impact on genome research. Furthermore, we consider particle-based peptide array fabrication using combinatorial chemistry. In this approach, particles can directly participate in both the synthesis and the transfer of synthesized combinatorial molecules to a substrate. Subsequently, we describe in more detail the synthesis of peptide arrays with amino acid polymer particles, which imbed the amino acids inside their polymer matrix. By heating these particles, the polymer matrix is transformed into a highly viscous gel, and thereby, imbedded monomers are allowed to participate in the coupling reaction. Finally, we focus on combinatorial laser fusing of particles for the synthesis of high-density peptide arrays. This method combines the advantages of particles and combinatorial lithographic approaches.

Microarrays doi: 10.3390/microarrays3040226

Authors: Takuya Matsumoto Holger Feroudj Ryosuke Kikuchi Yuriko Kawana Hidehiro Kondo Ikuo Hirono Toshiaki Mochizuki Yuji Nagashima Gen Kaneko Hideki Ushio Masaaki Kodama Shugo Watabe

Pufferfish accumulate tetrodotoxin (TTX) mainly in the liver and ovary. This study aims at investigating the effect of TTX accumulation in the liver of cultured specimens of torafugu Takifugu rubripes on the hepatic gene expression by microarray analysis on Day 5 after the intramuscular administration of 0.25 mg TTX/kg body weight into the caudal muscle. TTX was detected in the liver, skin and ovary in the TTX-administered individuals. The total amount of TTX accumulated in the body was 67 ± 8% of the administered dose on Day 5. Compared with the buffer-administered control group, a total of 59 genes were significantly upregulated more than two-fold in the TTX-administered group, including those encoding chymotrypsin-like elastase family member 2A, transmembrane protein 168 and Rho GTP-activating protein 29. In contrast, a total of 427 genes were downregulated by TTX administration, including those encoding elongation factor G2, R-spondin-3, nuclear receptor activator 2 and fatty acyl-CoA hydrolase precursor. In conclusion, our results demonstrate that the intramuscular administration of TTX changes the expression of hepatic genes involved in various signaling pathways.

Microarrays doi: 10.3390/microarrays3040212

Authors: Andrew Dalby Ian Bailey

The identification of genes responsible for causing cancers from gene expression data has had varied success. Often the genes identified depend on the methods used for detecting expression patterns, or on the ways that the data had been normalized and filtered. The use of gene set enrichment analysis is one way to introduce biological information in order to improve the detection of differentially expressed genes and pathways. In this paper we show that the use of network models while still subject to the problems of normalization is a more robust method for detecting pathways that are differentially overrepresented in lung cancer data. Such differences may provide opportunities for novel therapeutics. In addition, we present evidence that non-small cell lung carcinoma is not a series of homogeneous diseases; rather that there is a heterogeny within the genotype which defies phenotype classification. This diversity helps to explain the lack of progress in developing therapies against non-small cell carcinoma and suggests that drug development may consider multiple pathways as treatment targets.

Microarrays doi: 10.3390/microarrays3030203

Authors: Laura Astola Jaap Molenaar

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

Microarrays doi: 10.3390/microarrays3030180

Authors: Chandra Dixit Gerson Aguirre

Microfluidic-based micromosaic technology has allowed the pattering of recognition elements in restricted micrometer scale areas with high precision. This controlled patterning enabled the development of highly multiplexed arrays multiple analyte detection. This arraying technology was first introduced in the beginning of 2001 and holds tremendous potential to revolutionize microarray development and analyte detection. Later, several microfluidic methods were developed for microarray application. In this review we discuss these novel methods and approaches which leverage the property of microfluidic technologies to significantly improve various physical aspects of microarray technology, such as enhanced imprinting homogeneity, stability of the immobilized biomolecules, decreasing assay times, and reduction of the costs and of the bulky instrumentation.

Microarrays doi: 10.3390/microarrays3030168

Authors: Laura Huth Jörg Jäkel Edgar Dahl

Colorectal cancer, a clinically diverse disease, is a leading cause of cancer-related death worldwide. Application of novel molecular diagnostic tests, which are summarized in this article, may lead to an improved survival of colorectal cancer patients. Distinction of these applications is based on the different molecular principles found in colorectal cancer (CRC). Strategies for molecular analysis of single genes (as KRAS or TP53) as well as microarray based techniques are discussed. Moreover, in addition to the fecal occult blood testing (FOBT) and colonoscopy some novel assays offer approaches for early detection of colorectal cancer like the multitarget stool DNA test or the blood-based Septin 9 DNA methylation test. Liquid biopsy analysis may also exhibit great diagnostic potential in CRC for monitoring developing resistance to treatment. These new diagnostic tools and the definition of molecular biomarkers in CRC will improve early detection and targeted therapy of colorectal cancer.

Microarrays doi: 10.3390/microarrays3030159

Authors: Alberto La Spada Barnaba Rainoldi Andrea De Blasio Ida Biunno

There is virtually an unlimited number of possible Tissue Microarray (TMA) applications in basic and clinical research and ultimately in diagnostics. However, to assess the functional importance of novel markers, researchers very often turn to cell line model systems. The appropriate choice of a cell line is often a difficult task, but the use of cell microarray (CMA) technology enables a quick screening of several markers in cells of different origins, mimicking a genomic-scale analysis. In order to improve the morphological evaluations of the CMA slides we harvested the cells by conventional trypsinization, mechanical scraping and cells grown on coverslips. We show that mechanical scraping is a good evaluation method since keeps the real morphology very similar to those grown on coverslips. Immunofluorescence images are of higher quality, facilitating the reading of the biomarker cellular and subcellular localization. Here, we describe CMA technology in stem cell research.

Microarrays doi: 10.3390/microarrays3020137

Authors: Christoph Burdelski Aleksandra Matuszewska Martina Kluth Christina Koop Katharina Grupp Stefan Steurer Corinna Wittmer Sarah Minner Maria Tsourlakis Guido Sauter Thorsten Schlomm Ronald Simon

Molecular prognostic markers are urgently needed in order to improve therapy decisions in prostate cancer. To better understand the requirements for biomarker studies, we re-analyzed prostate cancer tissue microarray immunohistochemistry (IHC) data from 39 prognosis markers in subsets of 50 – >10,000 tumors. We found a strong association between the “prognostic power” of individual markers and the number of tissues that should be minimally included in such studies. The prognostic relevance of more than 90% of the 39 IHC markers could be detected if ≥6400 tissue samples were analyzed. Studying markers of tissue quality, including immunohistochemistry of ets-related gene (ERG) and vimentin, and fluorescence in-situ hybridization analysis of human epidermal growth factor receptor 2 (HER2), we found that 18% of tissues in our tissue microarray (TMA) showed signs of reduced tissue preservation and limited immunoreactivity. Comparing the results of Kaplan-Meier survival analyses or associations to ERG immunohistochemistry in subsets of tumors with and without exclusion of these defective tissues did not reveal statistically relevant differences. In summary, our study demonstrates that TMA-based marker validation studies using biochemical recurrence as an endpoint require at least 6400 individual tissue samples for establishing statistically relevant associations between the expression of molecular markers and patient outcome if weak to moderate prognosticators should also be reliably identified.

Microarrays doi: 10.3390/microarrays3020103

Authors: Ulrich Vogel

With the advent of new histopathological staining techniques (histochemistry, immunohistochemistry, in situ hybridization) and the discovery of thousands of new genes, mRNA, and proteins by molecular biology, the need grew for a technique to compare many different cells or tissues on one slide in a cost effective manner and with the possibility to easily track the identity of each specimen: the tissue array (TA). Basically, a TA consists of at least two different specimens per slide. TAs differ in the kind of specimens, the number of specimens installed, the dimension of the specimens, the arrangement of the specimens, the embedding medium, the technique to prepare the specimens to be installed, and the technique to construct the TA itself. A TA can be constructed by arranging the tissue specimens in a mold and subsequently pouring the mold with the embedding medium of choice. In contrast, preformed so-called recipient blocks consisting of the embedding medium of choice have punched, drilled, or poured holes of different diameters and distances in which the cells or tissue biopsies will be deployed manually, semi-automatically, or automatically. The costs of constructing a TA differ from a few to thousands of Euros depending on the technique/equipment used. Remarkably high quality TAs can be also achieved by low cost techniques.

Microarrays doi: 10.3390/microarrays3020091

Authors: Luca Quagliata Manuel Schlageter Cristina Quintavalle Luigi Tornillo Luigi Terracciano

Liver tumours are among the leading causes of cancer-related death worldwide and hepatocellular carcinoma (HCC) accounts for the vast majority of liver tumours. When detected at an early stage of disease, patients might still be eligible for surgical-based curative treatments. However, currently only small portion of HCC affected patients are diagnosed at an early stage. For late stage HCC no treatment option exists beside the multi-tyrosine kinase inhibitor Sorafenib. Thus new molecular targets and treatment options for HCC are urgently needed. Nevertheless, despite some improvements in diagnosis and patient management, the biology of liver tumour remains inadequately understood, mainly because these tumours have shown to harbour a highly complex genomic landscape. In addition, one major obstacle delaying the identification of new molecular targets in biomedical research is the necessity to validate them using a large collection of tissue specimens. Tissue microarray (TMA) technology allows the prompt molecular profiling of multiple tissue specimens and is therefore ideal to analyze presumptive candidate biomarkers in a fast an effective manner. The use of TMA has substantial benefits over standard techniques and represents a significant advancement in molecular pathology. For example, TMA technology reduces laboratory work, offers a high level of experimental uniformity and provides a judicious use of precious tissue. On the other hand, one potential limitation of using TMA is that the small cores sampled may not be representative of whole tumors. This issue is very critical in particularly heterogeneous cancers such as HCC. For liver focused studies, it is ideal to evaluate the staining patters of a determined marker over the structure of an entire acinus and to define staining in as many as possible anatomical regions. In this review we analyze the limits and opportunities offered by the usage of TMA technology in HCC research. In summary, TMA has revolutionized the histopathological analysis and will be of great help to further advance the knowledge in the field of hepatocarcinogenesis research.

Microarrays doi: 10.3390/microarrays3010089

Authors: Microarrays Editorial Office

The editors of Microarrays would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2013 [...]

Microarrays doi: 10.3390/microarrays3010072

Authors: Albert Chetcuti Nicole Mackie Siamak Tafavogh Nicole Graf Tony Henwood Amanda Charlton Daniel Catchpoole

Despite neuroblastoma being the most common extracranial solid cancer in childhood, it is still a rare disease. Consequently, the unavailability of tissue for research limits the statistical power of studies. Pathology archives are possible sources of rare tissue, which, if proven to remain consistent over time, could prove useful to research of rare disease types. We applied immunohistochemistry to investigate whether long term storage caused any changes to antigens used diagnostically for neuroblastoma. We constructed and quantitatively assessed a tissue microarray containing neuroblastoma archival material dating between 1950 and 2007. A total of 119 neuroblastoma tissue cores were included spanning 6 decades. Fourteen antibodies were screened across the tissue microarray (TMA). These included seven positive neuroblastoma diagnosis markers (NB84, Chromogranin A, NSE, Ki-67, INI1, Neurofilament Protein, Synaptophysin), two anticipated to be negative (S100A, CD99), and five research antibodies (IL-7, IL-7R, JAK1, JAK3, STAT5). The staining of these antibodies was evaluated using Aperio ImageScope software along with novel pattern recognition and quantification algorithms. This analysis demonstrated that marker signal intensity did not decrease over time and that storage for 60 years had little effect on antigenicity. The construction and assessment of this neuroblastoma TMA has demonstrated the feasibility of using archival samples for research.

Microarrays doi: 10.3390/microarrays3010052

Authors: Yi-Hsuan Chen Ru-Band Lu Hung Hung Po-Hsiu Kuo

Bipolar disorder is a complex psychiatric disorder with high heritability, but its genetic determinants are still largely unknown. Copy number variation (CNV) is one of the sources to explain part of the heritability. However, it is a challenge to estimate discrete values of the copy numbers using continuous signals calling from a set of markers, and to simultaneously perform association testing between CNVs and phenotypic outcomes. The goal of the present study is to perform a series of data filtering and analysis procedures using a DNA pooling strategy to identify potential CNV regions that are related to bipolar disorder. A total of 200 normal controls and 200 clinically diagnosed bipolar patients were recruited in this study, and were randomly divided into eight control and eight case pools. Genome-wide genotyping was employed using Illumina Human Omni1-Quad array with approximately one million markers for CNV calling. We aimed at setting a series of criteria to filter out the signal noise of marker data and to reduce the chance of false-positive findings for CNV regions. We first defined CNV regions for each pool. Potential CNV regions were reported based on the different patterns of CNV status between cases and controls. Genes that were mapped into the potential CNV regions were examined with association testing, Gene Ontology enrichment analysis, and checked with existing literature for their associations with bipolar disorder. We reported several CNV regions that are related to bipolar disorder. Two CNV regions on chromosome 11 and 22 showed significant signal differences between cases and controls (p < 0.05). Another five CNV regions on chromosome 6, 9, and 19 were overlapped with results in previous CNV studies. Experimental validation of two CNV regions lent some support to our reported findings. Further experimental and replication studies could be designed for these selected regions.

Microarrays doi: 10.3390/microarrays3010039

Authors: Stuart Baker

To gain biological insights, investigators sometimes compare sequences of gene expression measurements under two scenarios (such as two drugs or species). For this situation, we developed an algorithm to fit, identify, and compare biologically relevant response curves in terms of heteromorphy (different curves), heterochrony (different transition times), and heterometry (different magnitudes). The curves are flat, linear, sigmoid, hockey-stick (sigmoid missing a steady state), transient (sigmoid missing two steady states), impulse (with peak or trough), step (with intermediate-level plateau), impulse+ (impulse with an extra parameter), step+ (step with an extra parameter), further characterized by upward or downward trend. To reduce overfitting, we fit the curves to every other response, evaluated the fit in the remaining responses, and identified the most parsimonious curves that yielded a good fit. We measured goodness of fit using a statistic comparable over different genes, namely the square root of the mean squared prediction error as a percentage of the range of responses, which we call the relative prediction error (RPE). We illustrated the algorithm using data on gene expression at 14 times in the embryonic development in two species of frogs. Software written in Mathematica is freely available.

Microarrays doi: 10.3390/microarrays3010024

Authors: Xiaofei Wang Shannon Byers

DNA sequence variations include nucleotide substitution, deletion, insertion, translocation and inversion. Deletion or insertion of a large DNA segment in the genome, referred to as copy number variation (CNV), has caught the attention of many researchers recently. It is believed that CNVs contribute significantly to genome variability, and thus contribute to phenotypic variability. In chickens, genome-wide surveys with array comparative genome hybridization (aCGH), SNP chip detection or whole genome sequencing have revealed a large number of CNVs. A large portion of chicken CNVs involves protein coding or regulatory sequences. A few CNVs have been demonstrated to be the determinant factors for single gene traits, such as late-feathering, pea-comb and dermal hyperpigmentation. The phenotypic effects of the majority of chicken CNVs are to be delineated.

Microarrays doi: 10.3390/microarrays3010001

Authors: Hung-Ming Lai Sean May Sean Mayes

Genomic DNA-based probe selection by using high density oligonucleotide arrays has recently been applied to heterologous species (Xspecies). With the advent of this new approach, researchers are able to study the genome and transcriptome of a non-model or an underutilised crop species through current state-of-the-art microarray platforms. However, a software package with a graphical user interface (GUI) to analyse and parse the oligonucleotide probe pair level data is still lacking when an experiment is designed on the basis of this cross species approach. A novel computer program called Pigeons has been developed for customised array data analysis to allow the user to import and analyse Affymetrix GeneChip® probe level data through XSpecies. One can determine empirical boundaries for removing poor probes based on genomic hybridisation of the test species to the Xspecies array, followed by making a species-specific Chip Description File (CDF) file for transcriptomics in the heterologous species, or Pigeons can be used to examine an experimental design to identify potential Single-Feature Polymorphisms (SFPs) at the DNA or RNA level. Pigeons is also focused around visualization and interactive analysis of the datasets. The software with its manual (the current release number version 1.2.1) is freely available at the website of the Nottingham Arabidopsis Stock Centre (NASC).